CluVar:使用自编码器对变异进行集群,从单细胞RNA测序数据推断出癌症亚克隆
Chae Won Kim1, Heewon Park2, Dohyeon Kim1,3
1Department of Bioinformatics, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 06978, Republic of Korea.
Briefings in bioinformatics
|November 15, 2025
概括
一个新的自编码器框架CluVar,从单细胞RNA测序数据中重建了癌症亚克隆进化. 它克服了数据的局限性,准确地揭示了瘤的进化轨迹,并识别了癌症进展变异.
科学领域:
- 计算生物学 计算生物学
- 癌症基因组学 癌症基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤组织由于恶性亚克隆而表现出遗传异质性.
- 了解瘤进化是识别恶性特征的关键.
- 使用单细胞DNA测序 (scDNA-seq) 的现有亚克隆重建方法提供低分辨率,而单细胞RNA测序 (scRNA-seq) 数据容易产生噪音和脱落偏差.
研究的目的:
- 介绍CluVar,这是一个基于自编码器的框架,用于从scRNA-seq数据中推断癌症亚克隆基因.
- 为了应对scRNA-seq中缺少变异数据的挑战,以进行准确的遗传学重建.
主要方法:
- 开发了CluVar,这是一个自动编码框架,利用突变档案分析.
- 整合了一个定制的损失函数和多个隐藏层,以优化集群.
- 在各种错误条件下评估性能,并应用于真实癌症scRNA-seq数据.
主要成果:
- 克鲁瓦尔在重建癌症亚克隆的家族遗传树方面表现出卓越的表现.
- 该框架有效地处理了scRNA-seq数据中固有的缺失变异信息.
- 由CluVar生成的家族遗传树与癌症数据中的转录基因概况保持一致.
结论:
- 克鲁瓦提供了一种强大的方法,可以从scRNA-seq数据中推断癌症亚克隆的基因组.
- 该框架对于追踪瘤进化轨迹有价值.
- 克鲁瓦尔有助于识别与癌症进展相关的新型变异.
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